code: def _x_and_filters(draw, dim: int = 2):
      if not isinstance(dim, int):
          dim = draw(dim)
      strides = draw(
          st.one_of(
              st.lists(
                  st.integers(min_value=1, max_value=3),
                  min_size=dim,
                  max_size=dim,
              ),
              st.integers(min_value=1, max_value=3),
          )
      )
  
      pad_mode = draw(st.sampled_from(["valid", "same", "pad"]))
  
      padding = draw(
          st.one_of(
              st.integers(min_value=1, max_value=3),
              st.lists(
                  st.integers(min_value=1, max_value=2),
                  min_size=dim,
                  max_size=dim,
              ),
          )
      )
  
      batch_size = draw(st.integers(1, 5))
      filter_shape = draw(
          helpers.get_shape(
              min_num_dims=dim,
              max_num_dims=dim,
              min_dim_size=1,
              max_dim_size=5,
          )
      )
      dtype = draw(helpers.get_dtypes("float", full=False))
      input_channels = draw(st.integers(1, 3))
      output_channels = draw(st.integers(1, 3))
      group_list = [i for i in range(1, 3)]
  
      group_list = list(
          filter(lambda x: (input_channels % x == 0), group_list)
      )
  
      fc = draw(st.sampled_from(group_list))
      dilations = draw(
          st.one_of(
              st.lists(
                  st.integers(min_value=1, max_value=3),
                  min_size=dim,
                  max_size=dim,
              ),
              st.integers(min_value=1, max_value=3),
          )
      )
      full_dilations = (
          [dilations] * dim if isinstance(dilations, int) else dilations
      )
  
      x_dim = []
      for i in range(dim):
          min_x = filter_shape[i] + (filter_shape[i] - 1) * (
              full_dilations[i] - 1
          )
          x_dim.append(draw(st.integers(min_x, 15)))
      x_dim = tuple(x_dim)
  
      output_channels = output_channels * fc
      filter_shape = (
          output_channels,
          input_channels // fc,
      ) + filter_shape
  
      x_shape = (batch_size, input_channels) + x_dim
      vals = draw(
          helpers.array_values(
              dtype=dtype[0],
              shape=x_shape,
              min_value=0.0,
              max_value=1.0,
          )
      )
      filters = draw(
          helpers.array_values(
              dtype=dtype[0],
              shape=filter_shape,
              min_value=0.0,
              max_value=1.0,
          )
      )
      bias = draw(
          helpers.array_values(
              dtype=dtype[0],
              shape=(output_channels,),
              min_value=0.0,
              max_value=1.0,
          )
      )
  
      return (
          dtype,
          vals,
          filters,
          bias,
          dilations,
          strides,
          padding,
          fc,
          pad_mode,
      )
  
imports: from hypothesis import strategies
  import pytest
  import math
  import ivy_tests.test_ivy.helpers
  from ivy_tests.test_ivy.helpers import handle_frontend_test
  from ivy_tests.test_ivy.test_functional.test_nn.test_layers import _assume_tf_dilation_gt_1
  import ivy
label_desc: Use image node_id 6 for calling a global function with example usage: _x_and_filters(draw, dim) and returns: dtype,  vals,  filters,  bias,  dilations,  strides,  padding,  fc,  pad_mode